File:MATLABIndividualsChartForPairedIndividualsAndMRChart.png

#!/usr/bin/perl -w

#
# randomly generate process observations that simulate a
# normally-distributed process in the state of statistical control
# (i_setup.csv) and simulate the same process experiencing a drift of
# magnitude $drift starting two hours into the $shift shift
# (i_monitoring.csv)
#

use strict;
use Math::Random;

my %shiftSchedule = (
    "first" =>  { "start" =>  6.00, "end" => 14.00 },
    "second" => { "start" => 14.00, "end" => 22.00 },
    "third" =>  { "start" => 22.00, "end" =>  6.00 }
);
my $shift = "third";         # shift to monitor
my $inspectionRate = 1 / 2;  # every 1/2 hour
my $drift = 1.5;             # drift to simulate
my $m = 25;                  # samples in control chart setup
my $target = 100.0;          # quality characteristic target

my $i;
my $observation;
my $setupM = $m;

#
# simulate control chart setup
#
open(SETUPCSV, ">i_setup.csv") || die "! can't open \"i_setup.csv\" ($!)\n";
for ($i = 1; $i <= $m; $i++) {
    $observation = $target + random_normal();
    print SETUPCSV $observation . "\r\n";
}
close(SETUPCSV);

#
# simulate control chart monitoring
#
open(MONITORINGCSV, ">i_monitoring.csv") || die "! can't open \"i_monitoring.csv\" ($!)\n";

if ($m < 0) {
    $m += 24;
}
$m /= $inspectionRate;
for ($i = 1; $i <= $m; $i++) {
    $observation = $target + random_normal();
    if ($i >= (0.25 * $m)) {
        if ($i < (0.75 * $m)) {
            $observation += ($drift / (0.5 * $m)) * ($i - (0.25 * $m));
        } else {
            $observation += $drift;
        }
    }
    printf MONITORINGCSV "U%d,%7.3f\r\n", 2 * $setupM + $i, $observation;
}
close(MONITORINGCSV);